Time-slide window join over data streams

The join is an important operator in processing data streams. To produce outputs continuously over unbounded data streams, sliding windows are generally used to limit the scope of the join at a certain time. In the existing join algorithms, only a simple type of windows have been considered, which are updated whenever a new data item arrives on any input stream. On the other hand, a more common type of windows have not been addressed yet, whose intervals are updated periodically, i.e., slid by a predefined time interval. In this paper, we consider the time-slide windows in joining multiple data streams. The algorithm for the time-slide window join can vary according to (i) how frequently the join is evaluated and (ii) which structure is used for windowing. Regarding this, possible algorithms are discussed, and experimental results that compare their performances are provided in this paper.     

Collaborative representation with reduced residual for face recognition

Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %.     

甲基对苯二酚的合成进展

综述了甲基对苯二酚的各种制备方法,并比较了各自的优缺点.着眼于反应条件温和,环境友好的发展方向及选择高效的催化剂,指出双氧水直接羟基化邻甲酚制备甲基对苯二酚是最具有潜力的工艺方法.     

注射螺杆螺棱结构对计量段物料输送性能的研究

运用有限元分析软件POLYFLOW,根据聚碳酸酯物料注塑成型的实际工作状态,对PC物料在矩形断面和锯齿形断面流道中的流动进行了三维等温非牛顿流场模拟计算,在得到速度场的基础上对计算结果进行了后处理,分析了不同螺杆构型的输送能力,分布混合能力以及物料在不同螺杆构型中的流动状况.     

Estimation of parameters where dependent observations are related by equality constraints

Pirt's model for microbial growth and product formation are reparameterized to obtain multiresponse models with common parameters. The dependent variables in the models are related through the available electron and carbon balance constraints. Covariance adjustment is used to reduce the growth model to a unit variate linear model with covariates. Therefore, standard multiple regression programs can be used to obtain combined point and interval estimates of true biomass energetic yield, true product yield and maintenance coefficient. This approach may yield “better” estimates than the maximum likelihood approach when an appropriately selected subset of covariates is used. Nonlinear estimation procedures are also considered; these procedures are efficient with few responses; however, as the number of responses per observation increase, they may require a lot of computing time. For illustration several data from the biochemical engineering literature are analyzed by the proposed methods.     

基于支持向量机的TiO_2/C复合气凝胶光催化性能预报

TiO_2/C复合气凝胶光催化性能与材料的组成、孔结构参数密切相关,本文利用支持向量机的方法建立材料原始合成配方与TiO_2/C复合气凝胶光催化性能之间的定量预报模型。SVR建模和留一法交叉验证所得材料降解率计算值与实验值之间的相关系数平方(R~2)分别为0.929和0.884。利用该模型预报了2个新的实验样本,其降解率预报结果和实验结果非常接近,相对误差小于4%。     

分子动力学模拟聚乙烯在碳纳米管表面的扩散

通过采用分子动力学方法模拟不同链长的聚乙烯分子在单壁碳纳米管表面的扩散,探究了聚乙烯的动力学性质。研究表明随着链长的增加聚乙烯在碳纳米管表面的扩散系数减小,且二者间存在明显的标度关系。聚乙烯在碳纳米管表面扩散的扩散系数和聚乙烯吸附在碳纳米管表面的构象有关,有序结构的聚乙烯比无序结构的聚乙烯在碳纳米管表面扩散的快。此外,由于受到碳纳米管吸附作用的影响,聚乙烯分子在平行于管轴和垂直于管轴2个方向上的扩散系数不同,扩散表现各向异性。     

固体超强酸S_2O_8~(2-)/Fe_2O_3—CoO催化合成乙酸异戊酯

制备固体超强酸催化剂S2O82-/Fe2O3—CoO,并用于乙酸异戊酯合成反应。用该催化剂通过正交试验合成乙酸异戊酯的最佳条件为:醇酸物质的量比为1.5,催化剂用量为0.8 g(以0.2 mol乙酸为准),带水剂用量为10 mL,反应时间为2.5 h,其酯化率可达98%以上。